Identify a classic bagging algorithm
Company: C3 AI
Role: Data Scientist
Category: Machine Learning
Difficulty: easy
Interview Round: Technical Screen
# Identify a classic bagging algorithm
### Multiple choice: Bagging
Which of the following algorithms is a classic example of **bagging** (bootstrap aggregating)?
A. Random Forest
B. Gradient Boosting
C. Logistic Regression
D. Support Vector Machine
### Clarifying Questions to Ask
- Clarify the task, data shape, labels, constraints, and evaluation metric.
- State assumptions behind the math or modeling technique you choose.
- Connect theory to practical training, debugging, and deployment implications.
### What a Strong Answer Covers
- Correct definitions and formulas where the prompt requires them.
- A practical explanation of how the method behaves on real data.
- Trade-offs, failure modes, diagnostics, and mitigation strategies.
- Evaluation choices that match the product or modeling objective.
### Follow-up Questions
- How would noisy labels, class imbalance, or distribution shift affect the answer?
- What would you monitor after deployment?
- Which baseline would you compare against first?
Overview: This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Identify a classic bagging algorithm states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.